Problem solving method utilizing emergent clustering and case retrieval

ABSTRACT

An Adaptive, Any-Time Case Retrieval process combines existing knowledge (emergent clustering, case retrieval with CRN) in a novel and advantageous way. Although ideally suited to the Digital Body Development System (DBDS), the method is applicable to any problem solving environment involving iterative simulations. Given a description of a problem to be solved, a candidate solution is applied to generate a problem solving base case. The description of the problem is modified and the modification is recorded. Candidate solutions are applied to the problem as modified, thereby generating a problem solving event case. These steps are repeated so as to rank the relevance of the cases generated to arrive at an optimal solution to the problem. In the preferred embodiment a case retrieval network (CRN) structure is used in the ranking process.

FIELD OF THE INVENTION

This invention relates generally to problem solving and, in particular,to a method utilizing emergent clustering and case retrieval.

BACKGROUND OF THE INVENTION

The Digital Body Development System (DBDS) was launched in 2003. Theproject has the potential to save U.S. automobile manufacturers $3.5billion in vehicle launch costs by shortening lead times, reducing thenumber of physical evaluation model builds, and improving the quality ofvehicle body assemblies. The four-year project will involve significantnew development of engineering software systems and will culminate in avalidation phase implemented at two vehicle launches—one at Ford MotorCompany and one at General Motors Corporation.

The project is a joint venture between Altarum Institute and the Centerfor Automotive Research (CAR). The joint venture includes aninter-disciplinary team consisting of auto companies (General Motors,Ford), software providers (EDS/UGS-PLM), die tooling, foundries,assembly tooling, metrology equipment providers, and other researchorganizations (Wayne State University and University of Michigan).

DBDS will enable the implementation of a virtual functional buildmethodology to help designers and vehicle launch teams make betterdecisions faster and understand the quality, cost, and timing impacts ofthose decisions. Modules under development identify problems in thecurrent design of a vehicle body, suggest potential changes, andevaluate these changes with respect to their impact on manufacturabilityand expected cost.

DBDS takes a specific design of a car body or sub-assembly in the launchphase of a new vehicle, analyzes it for deviations from its functionalspecification, and seeks to determine changes to the design that reduceor remove these deviations (FIG. 1). The design provided to the DBDS iscalled “base design,” and includes information about individual parts,their assembly process, and tools and fixtures used during the assembly.Once the launch team implements some or all of the suggested changes,DBDS receives the new base design and repeats the analysis andimprovement process until the functional specification is met.

DBDS analyzes a given design by simulating the specified assemblyprocess, using an existing assembly simulation (currently Vis-VSA byUGS/PLM). Deviation from the functional specification is measured on thevirtual end product of the simulated assembly.

If the base design deviates from the functional specification, DBDS willbegin generating alternative solution candidates, which are sets ofchanges to the base design that may be suggested to the launch team.DBDS applies the design changes represented by a new solution candidateto the base design, creating a new design. This new design is againanalyzed for its deviation from the functional specification, using theassembly simulation. If the new design is a sufficient improvementcompared to the base design, then DBDS will propose the solutioncandidate (set of changes) to the launch team. Otherwise, it willcontinue generating and evaluating new solution candidates (FIG. 2).

Algorithmically, the repeated generation and evaluation of new solutioncandidates utilizes a heuristic search process through thehigh-dimensional space of possible design changes, guided by a utilityfunction that is based on the degree to which the new design reducesdeviations from the functional specification compared to the basedesign.

SUMMARY OF THE INVENTION

This invention improves upon existing techniques by providing anAdaptive, Any-Time Case Retrieval process that combines existingknowledge (emergent clustering, case retrieval with CRN) in a novel andadvantageous way. Although ideally suited to the Digital BodyDevelopment System (DBDS), the method is applicable to any problemsolving environment involving iterative simulations.

Given a description of a problem to be solved, a candidate solution isapplied to generate a problem solving base case. The description of theproblem is modified and the modification is recorded. Candidatesolutions are applied to the problem as modified, thereby generating aproblem solving event case. These steps are repeated so as to rank therelevance of the cases generated to arrive at an optimal solution to theproblem.

In the preferred embodiment a case retrieval network (CRN) structure isused in the ranking process, and information is exchanged through ashared dynamic environment that represents the space of possible problemconfigurations. The shared dynamic environment may be implemented with aPheromone Infrastructure in which the information is deposited,modified, and retrieved by independent processes.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram that illustrates how DBDS improves a given design toreduce deviations from functional specification;

FIG. 2 is a diagram that shows how the improvement of the base design isan iterative process that generates and evaluates alternative solutioncandidates;

FIG. 3 depicts a search agent decision loop as developed in DARPA ANT;

FIG. 4 shows how RAADSI's enhanced agent decision process dynamicallydecides between short-range moves and long-range jumps; and

FIG. 5 depicts the components of an Adaptive, Any-Time Case RetrievalSolver according to the invention.

DETAILED DESCRIPTION OF THE INVENTION

Having discussed the high-level DBDS architecture in the Background ofthe Invention, in the following Detailed Description we discuss themeta-search heuristics, followed by the Adaptive Any-Time Case Retrievalalgorithm.

Meta-Search Heuristic

APSE (Adaptive Parameter Search Engine)

The Adaptive Parameter Search Engine (APSE) guides the search forsettings of input parameters of a simulation model that result indesired dynamics to arise in the execution of the simulation. Each suchconfiguration setting is considered a solution candidate in APSE. Theengine comprises an agent population (search agents) and a datamanagement structure for solution candidates (search space). Individualsearch agents generate new solution candidates, which are stored in thesearch space management structure.

A search agent has a unique location in search space that may changeover time. A location in search space in APSE is a particular solutioncandidate that the search agent either created or adopted in its latestre-location step. It is the goal of the agent to find a location insearch space (solution candidate) that carries a high fitnessevaluation. The fitness of a location is a scalar numericalrepresentation of the degree to which the execution of the correspondingsolution candidate results in observed dynamics in the simulation thatis configured by the candidate. The user of APSE specifies the fitnessfunction. A measure of confidence is generated regarding whether theobserved dynamics indicate a phase change in the model.

The cyclical decision process of the individual agent is very simple,but repeated across the entire population, it results in an effectiveparallel search process that seeks to minimize the expenditure ofcomputing resources (simulation runs) while maximizing the fitness ofthe best known solution candidate. Each agent repeatedly executes thesame decision process as depicted in FIG. 3. First, the agent decideswhether to contribute to the fitness evaluation of its current solutioncandidate (“sample”), or whether to create or adopt a new solutioncandidate (“re-locate”).

Evaluating the fitness of a solution candidate entails executing thesimulation with the parameter settings provided by the solutioncandidate. If the underlying simulation model is deterministic, thenonly one simulation run is required per solution candidate and the agentwill only consider the “sample” option, if this simulation run had notyet been executed. However, if the simulation is non-deterministic,additional runs will improve the confidence level of the fitnessevaluation. In this case, the agent tends to choose the “sample” optionif the current confidence is relatively low. Furthermore, locations witha low confidence level are more likely to receive additional simulationruns if their (roughly) estimated fitness is high, since they “look morepromising.”

If the agent decides against further sampling at its current location,it will seek to replace its current solution candidate with a differentone. Thus, the agent will re-locate in search space. In APSE, the agentassumes its new location through a short-range move, which is arelatively small modification to its current solution candidate. Theagent determines the “direction” of the move—which aspects of thesolutions are changed how—in combination of a local hill-climbingapproach and a Particle Swarm Optimization (PSO) approach. It samplesthe fitness of existing, but not necessarily occupied, nearby locationsand it samples the fitness of locations currently occupied by othersearch agents. The agent is attracted towards locations that have asignificantly higher fitness then the agent's current location. Theweighted sum of these attractive forces determines the direction of theagent's move, but the length of the step is limited to a small amount(incremental change). If the re-location of the agent takes it to alocation that had not been occupied by any other agent before, then anew solution candidate is created. Otherwise, the agent adopts anexisting candidate. In APSE, we choose the initial location of thesearch agents randomly.

RAADSI (Resource-Aware Adaptive Dynamic Search Infrastructure)

RAADSI is similar to APSE in that it explores alternative inputconfigurations to a simulation (modified base designs) and evaluatestheir fitness based on observations from the simulation (divergence fromfunctional specification). The exploration of solution candidates isguided by a population of search agents and a solution candidate is aset of changes to the base design that produce the modified designtested in the simulation.

The APSE heuristic guides the search of the agents and thus theirgeneration of new solution candidates solely based on the abstractfitness landscape spanned over the search space. As such, it iscompletely domain and problem independent, similar to genetic orevolutionary optimization methods.

Using a problem independent heuristic is a good first approach that maysuffice in many less complex search spaces. But to cope with morecomplex optimization problems, such as the DBDS one, additional problemsolution knowledge should be brought to bear. Furthermore, knowledgeabout solution candidates that have already been tried out (simulated)will also help guide the search of the agents.

We enhanced the decision process of the search agent to distinguishbetween short-range moves that slightly modify the agent's solutioncandidate and long-range jumps that replace the solution candidate witha completely unrelated one. With short-range moves, an agent exploresits current region in search space, just as the APSE agents do, butthese modifications may also take observations from the simulation ofthe current solution candidate into account. With long-range jumps, anagent abandons its current region of the search space for a newlocation, essentially beginning a new exploration cycle.

The enhanced decision cycle in RAADSI is depicted in FIG. 4. The DBDSarchitecture includes Modifier instances that apply their respectivelogic to suggest short-range moves and Solver instances that proposelong-range jumps to the agents.

The new search agent begins its decision process with the “sample” or“re-locate” decision, which determines whether the agent will create oradopt a new solution candidate or add another sample to its currentlocation's fitness estimate. This decision is still taken in the APSEway.

If the agent decides to re-locate, it will now examine the rate offitness improvement that it experienced in the recent past. If this ratewas high, then the agent considers its current region of the searchspace sufficiently promising enough to explore it some more and it willre-locate in a short-range move to a nearby location. Otherwise, theagent will consult the currently available Solver instances to receive anow solution candidate for a long-range jump.

Solvers are applying problem solving knowledge to the faults observed inthe base design to generate new solution candidates. As the searchagents continue to explore the space of possible design changes, Solversmay take observations from these trials into account when they propose asolution. Thus, dynamically integrating problem solving knowledge intothe search process will enhance its performance. One such novel Solveralgorithm is discussed in the next section.

Adaptive Any-Time Case Retrieval Solver

The essence of the invention resides in a novel approach to dynamicallyretrieve cases of past problem solving events from a case base. The caseretrieval system is an any-time process that runs parallel to thedistributed agent search and takes into account the characteristics ofthe original problem, as well as the quality of solution candidates thathave already been tried out. This adaptive, any-time case retrievalalgorithm will be integrated into the DBDS architecture as a Solverinstance.

The Solver implementing the case retrieval process has two majorcomponents that establish a tight feedback loop (FIG. 5). The firstcomponent is the modification of the description of the current problemto explore the surrounding space of similar configurations. The secondcomponent is the case relevance ranking that establishes the respectiverelevance of the recorded cases with respect to the current modificationof the problem description. These two components alternate in theirexecution, exchanging information through a shared dynamic environmentthat represents the space of possible problem configurations. Thisenvironment is preferably a Pheromone Infrastructure (PI) in whichinformation is deposited, modified, and retrieved by independentprocesses. The PI bins the space spanned by a subset of the dimensionsthat are occupied by the anchor points. Information about thedescription of the current problem is deposited onto these bins and thusbecomes available as the problem signature.

The modification of the current problem description explores similarproblem descriptions, which in turn modifies the degree, to whichindividual cases match to the problem. We measure the quality of theretrieval from the case base under a given problem description by theentropy of the selection probabilities of the individual cases. Theseprobabilities derive from the normalized similarity of the problemdescription recorded in the respective cases with the current problemdescription. If the Adaptive Any-Time Case Retrieval Solver is beingasked to deliver a solution to the current problem, it will select acase from the case base probabilistically, using the associatedprobabilities, and extract and return the solution recorded in thishistorical case. This solution is then tried out (preferably usingsimulation) and evaluated by assigning a numerical score. The score isreturned to the Solver to further influence the problem modificationprocess.

We use a Case Retrieval Network (CRN) structure to represent therelevance of specific components of problem descriptions to recordedsolution cases. Our current CRN model is taken directly from theresearch publications of M. Lenz and H. D. Burkhardt at HumboldtUniversity Berlin, Germany.

The problem encountered with a specific design is represented as apattern of Anchor points defined on the virtual assembly model. AnAnchor point has a specific geometric location and additionalinformation, such as the assembly context or the magnitude of deviationfrom specification at the Anchor location define its location in otherdimensions of the problem space.

The problem description modification process performs an ongoingemergent clustering of the Anchor points in the high-dimensional problemspace. Although an ant-based clustering mechanism is preferred, wesettled on a simple force model that treats individual Anchor points asactive agents that are attracted to nearby Anchors and to theirrespective original location. The emergent pattern of Anchor clusters isinfluenced by the resulting quality of the case retrieval and thelocation of problem description components of cases whose solutions havealready been tried out by the search agents.

Our CRN comprises information entity (IE) nodes that represent observedlocations of anchor point clusters, linked to case nodes that representthe historical case in which these clusters were observed as well as adescription of the solution that has solved the problem encountered inthis historical case. The current arrangement of anchor point clustersas conveyed by the Pheromone Infrastructure places an activation ontothe IE nodes inverse proportional to the distance between the observedcluster in the current signature and the cluster location represented bythe respective IE. The links between the IE nodes and the case nodespropagates this activation to the case nodes and the resultingactivation of the case nodes determines the selection probability of theparticular case.

1. A method of selecting historical problem/solution cases for a designhaving a case base, comprising the steps of: a) creating an arrangementof anchor points that combine observed deviations from functionalspecifications at predefined measurement points on the design; b)providing a set of historical problem/solution cases associated with thedesign; c) modifying the arrangement of the anchor points and extractinga dynamic problem signature from the new arrangement; d) ranking the setof historical problem/solution cases according to their similarity tothe extracted signature; e) repeating steps c) and d) until rank scoringis of sufficient quality; f) selecting a problem/solution case based onits rank score; g) evaluating the selected problem/solution case basedupon divergence from the functional specifications; and h) repeatingsteps c) through f) as necessary to identify one or moreproblem/solution cases applicable to the design.
 2. The method of claim1, wherein the anchor points include additional contextual datadescribing the problem domain.
 3. The method of claim 1, wherein thestep of selecting a problem/solution case is carried outprobabilistically.
 4. The method of claim 1, wherein the step ofevaluating a selected case uses a resource-aware adaptive dynamic searchinfrastructure.
 5. The method of claim 1, wherein the step of modifyingthe arrangement of the anchor points is based upon emergent clustering.6. The method of claim 5, wherein the anchor points are treated asactive clustering agents that relocate according to dynamically computedvirtual forces.
 7. The method of claim 6, wherein the clustering agentsare attracted to other, nearby agents as well as their own anchor point.8. The method of claim 1, wherein the dynamic problem signature is theset of locations of the clusters of agents at a particular point intime.
 9. The method of claim 8, wherein the location of clusters isdetermined through discrete binning of clustering agent locations. 10.The method of claim 5, wherein the Shannon Entropy of the case selectionprobabilities changes the forces in the emergent clustering.
 11. Themethod of claim 5, wherein the evaluation of a selected case changes theforces in the emergent clustering.
 12. The method of claim 9, whereinthe anchor point locations are binned by a pheromone infrastructure (PI)and repeated deposits of digital pheromones at the locations of anchorpoints.
 13. The method of claim 12, wherein signature locations arerecorded as a subset of locations of bins in the PI.
 14. The method ofclaim 1, wherein each historical case has a recorded problem signature.15. The method of claim 1, wherein the historical cases are arranged ina case retrieval network (CRN).
 16. The method of claim 15, wherein theCRN includes information entity nodes representing observed locations ofclusters of anchor points.
 17. The method of claim 15, wherein the CRNis activated by the location of the clusters of anchor points defined inthe current problem signature.
 18. The method of claim 15, wherein therank score of each historical case is determined by its currentactivation in the CRN.
 19. The method of claim 1, wherein theprobability of selecting a historical case is proportional to its rankscore.
 20. The method of claim 1, wherein the quality of the rankscoring is the Shannon Entropy of the selection probabilities associatedwith the cases in the case base.
 21. The method of claim 1, wherein theevaluation of a selected case includes a numerical score.